Efficient Collection of Connected Vehicle Data based on Compressive Sensing

TL;DR

This paper presents a real-time compressive sensing method for connected vehicle data that significantly reduces data volume while maintaining high accuracy in data recovery, enhancing transportation data management.

Contribution

The paper introduces a novel real-time compressive sensing approach tailored for connected vehicle data collection and demonstrates its effectiveness on large-scale speed data recovery.

Findings

Achieves a compression ratio of 0.2 with low RMSE of 0.05

Performs better with steady or smoothly changing vehicle speeds

Successfully recovers 10 million CV speed samples

Abstract

Connected vehicles (CVs) can capture and transmit detailed data like vehicle position, speed and so on through vehicle-to-vehicle and vehicle-to-infrastructure communications. The wealth of CV data provides new opportunities to improve the safety, mobility, and sustainability of transportation systems. However, the potential data explosion likely will overburden storage and communication systems. To solve this issue, we design a real-time compressive sensing (CS) approach which allows CVs to collect and compress data in real-time and can recover the original data accurately and efficiently when it is necessary. The CS approach is applied to recapture 10 million CV Basic Safety Message speed samples from the Safety Pilot Model Deployment program. With a compression ratio of 0.2, it is found that the CS approach can recover the original speed data with the root mean squared error as low as 0.05. The recovery performances of the CS approach are further explored by time-of-day and acceleration. The results show that the CS approach performs better in data recovery when CV speeds are steady or changing smoothly.